Integrated Retail and Wholesale Power System Operation

with Smart Grid Functionality

Auswin George Thomasa, Pedram Jahangirib, Chengrui Caic,

Huan Zhaod, Dr. Dionysios Aliprantise, Dr. Leigh Tesfatsionf

ECpE{a,b,c,e,f} and Economics{d,f}, Iowa State University

(agthomas, pedramj, ccai, hzhao, dali, tesfatsi)@iastate.edu

Project Homepage: www.econ.iastate.edu/tesfatsi/irwprojecthome.htm

In this project we are developing the IRW Power System Test Bed, an agent-based test bed permitting the integrated study of retail and wholesale power systems operating over realistically

rendered transmission and distribution grids. This test bed seams together AMES, an open source agent-based test bed developed at Iowa State University that models a restructured wholesale

power market, and GridLAB-D, an open source electric energy distribution platform that simulates end-user load with great detail. Research topics under study by means of the IRW test bed

include: the reliability and efficiency implications of introducing price-sensitivity of demand for retail customers as realized through demand response, demand dispatch, and/or price-sensitive

demand bidding; the dynamic effects of increased penetration of consumer-owned distributed energy resources, such as PV generation and plug-in electric vehicles; and the development of

agent-based algorithms for smart device implementation.

Abstract

The IRW project is supported in part by the Electric Power Research Center of Iowa State

University and the Pacific Northwest National Laboratory (PNNL).

Execution Steps: AMES and GridLAB-D A Household with an

Intelligent HVAC System

Conclusion Initial Test Case

Introduction Modeling of Households in GridLAB-D

Acknowledgement

The IRW test bed seams together AMES (Agent-

Based Modeling of Electrical Systems) and

GridLAB-D. AMES is a modular agent-based

computational laboratory based on the actual

design of US restructured wholesale power

markets. The agents in AMES include an

Independent System Operator (ISO), Generating

Companies (GenCos), and Load Serving Entities

(LSEs). The GenCos and the LSEs participate in

a two-settlement system consisting of a day-

ahead and a real-time market operated and

settled by the ISO. Congestion is managed by

Locational Marginal Prices (LMPs). The actual

load for the real-time market arises from

GridLAB-D, a modular agent-based energy

distribution platform. GridLAB-D models

residential, industrial and commercial retail

consumers with a variety of appliances and

equipment.

Structure of the test bed

The first version of the test bed

consists of four main components,

GridLAB-D, Data Management

Program, MySQL database server

and AMES. These components

communicate via a local area

network, and can be placed on

systems running different operating

systems, thus increasing overall

flexibility.

Modeling a typical household with HVAC, water heater, light, TV,

fan, and plug-in loads during one typical 24-hour summer day

(July first)

Variation of indoor temperature set-points by a

household trying to minimize energy consumption based on a fixed price

Variation of indoor temperature set-points by a

household trying to minimize energy consumption based on dynamic LMPs

Key tasks to be addressed in this project include:

1) Extension of the LSEs in the AMES wholesale power market to enable them to

aggregate, service and settle the load coming from their GridLAB-D retail customers.

2) Development of a retail distribution module that exploits the capabilities of GridLAB-D

for simulating retail load arising from a wide variety of appliances and equipment as well

as retail generation arising from consumer-owned distributed energy resources such as

PV panels.

3) Development of a communication system (a data management program plus a MySQL

database server) permitting two-way communication between AMES wholesale operations

and GridLAB-D retail operations.

4) Extension of the LSEs in AMES to enable them to forecast price-sensitive loads and

submit demand bids in the wholesale power market corresponding to these forecasted

loads.

Extension of a generic load bus in AMES

to include retail downstream customers

AMES

starts

Initialize the DA

price for day 1

D = 1

i = 1

Send DA price for

day D to database

Query load of ith interval

in day D from database

Calculate the real time

price for ith interval and

save to database

Yes

success? No

i = i + 1

i < I*

D = D + 1

No

Yes

Calculate the DA

price for day D

No

exit

Yes

D < D*

DMP

starts

Initialization

D = 1; i = 1

Query DA price for

day D from database

Call GridLAB-D

to simulate load for

ith interval in day D

Send load for this

interval to database

i = i + 1

i < I*

Yes

success? No

Yes

D = D + 1

No

D < D*

Yes

No